Handwriting recognition is a technique that enables a computing device to interpret handwritten text or handwritten input. A typical handwriting recognition method involves scanning a handwritten document to generate an electronic document that includes handwritten text. In the electronic document, words are identified and extracted. Further, the characters in the extracted words are identified and extracted. Thereafter, various parameters associated with the way the characters have been written are determined. The various parameters may include, but are not limited to, a percentage of pixels above the horizontal half point, a percentage of pixels to right of the vertical half point, a number of strokes, a slope associated with the extracted characters, etc.
Most of the commonly known handwriting recognition methods employ various machine-learning algorithms such as, but not limited to, neural networks and support vector machines (SVM). Such handwriting recognition methods utilize a repository of handwriting styles to recognize handwriting in the electronic document. However, recognizing handwriting using such handwriting recognition methods may be limited to the robustness of the repository (i.e., variations in handwriting styles).